TTS

Text-to-speech (TTS) is a category of assistive and AI-driven speech synthesis technologies that convert written text into spoken language. TTS systems are used to provide audible output for devices and services, improving accessibility for people with visual impairments or reading difficulties, and enabling voice interactions in applications such as virtual assistants, navigation systems, and automated customer service.

A typical TTS pipeline includes text processing (normalization, abbreviation expansion, punctuation, tokenization), linguistic analysis (grapheme-to-phoneme conversion, prosody prediction), and speech synthesis (generation of an acoustic waveform). Many systems support SSML to control speaking rate, pitch, volume, and emphasis.

Synthesis methods include concatenative synthesis, which stitches together prerecorded speech units; parametric methods, which generate voice using statistical models; and neural TTS, which uses neural networks to directly generate waveforms or spectrograms, producing more natural-sounding voices and easier voice adaptation. Neural TTS often employs end-to-end architectures and vocoders such as WaveNet, WaveRNN, or HiFiGAN.

TTS supports multiple languages and voices, often with customizable speaker traits and speaking styles. Applications span accessibility tools, education, media accessibility, automotive infotainment, and customer support chatbots. Evaluation typically uses objective measures and perceptual tests, including intelligibility, naturalness, and listener-based MOS (mean opinion score).

Challenges include pronunciation of ambiguous text, homographs, prosody and emotion, latency, data privacy, and the need for high-quality, diverse training data to cover languages and dialects. Ongoing research focuses on more expressive, controllable, and expressive TTS, better multilingual support, and reducing computational requirements for on-device deployment.